code: def test_transform(kind):
      X, y = make_classification(chunks=100)
  
      if kind == "numpy":
          X, y = dask.compute(X, y)
      elif kind == "dask.dataframe":
          X = dd.from_dask_array(X)
          y = dd.from_dask_array(y)
  
      base = PCA(random_state=0)
      wrap = ParallelPostFit(PCA(random_state=0))
  
      base.fit(*dask.compute(X, y))
      wrap.fit(*dask.compute(X, y))
  
      assert_estimator_equal(
          wrap.estimator, base, exclude="n_features_"
      )
  
      result = base.transform(*dask.compute(X))
      expected = wrap.transform(X)
      assert_eq_ar(result, expected)
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier
  from sklearn.naive_bayes import CategoricalNB
  from sklearn.preprocessing import OneHotEncoder
  from dask_ml.datasets import make_classification
  from dask_ml.utils import assert_eq_ar, assert_estimator_equal
  from dask_ml.wrappers import ParallelPostFit
label_desc: Use image node_id 9 for calling a global function with example usage: test_transform(kind) without return types